mcp_omni_connect

<p align="center"> <img src="assets/IMG_5292.jpeg" alt="OmniCoreAgent Logo" width="250"/> </p> <h1 align="center">OmniCoreAgent</h1> <p align="center"> <strong>The Open Production Agent Harness for Python</strong><br /> <em>Parallel tool batches, structured observations, signature loop detection, MCP tools, memory, workspace files, subagents, background tasks, and REST/SSE serving.</em> </p> <p align="center"> <a href="https://pepy.tech/projects/omnicoreagent"><img src="https://static.pepy.tech/badge/omnicoreagent" alt="PyPI Downloads"></a> <a href="https://badge.fury.io/py/omnicoreagent"><img src="https://badge.fury.io/py/omnicoreagent.svg" alt="PyPI version"></a> <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="Python Version"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-green.svg" alt="License"></a> </p> <p align="center"> <a href="#what-it-is">What It Is</a> - <a href="#quick-start">Quick Start</a> - <a href="#choose-your-path">Choose Your Path</a> - <a href="#what-you-can-build">Use Cases</a> - <a href="#why-it-matters">Why It Matters</a> - <a href="#install-only-what-you-need">Install</a> - <a href="./cookbook">Cookbook</a> - <a href="#features">Features</a> - <a href="https://docs-omnicoreagent.omnirexfloralabs.com/docs">Docs</a> - <a href="https://docs-omnicoreagent.omnirexfloralabs.com/docs/getting-started/use-docs-with-ai-tools">Ask AI</a> </p> --- ## What It Is An LLM is not an agent by itself. The model provides intelligence; the harness gives that intelligence a working environment. OmniCoreAgent is the application-facing harness layer around a model: ```text model + prompt contract + reasoning loop + local tools + MCP tools + parallel tool batches + structured observations + memory + context control + workspace files + tool-output offloading + guardrails + events + subagents + background tasks + REST/SSE serving ``` That is the difference between an agent harness and a generic agent library. A library gives you pieces to assemble. A harness gives you the runtime boundary that makes a model usable inside an application. OmniCoreAgent keeps that boundary explicit: | Layer | What It Owns | |-------|--------------| | **Agent harness** | Model loop, prompt contract, tools, observations, memory, context, workspace, guardrails, events, subagents | | **Serving boundary** | OmniServe REST/SSE APIs, request lifecycle, readiness, auth, rate limits, metrics | | **Background boundary** | Durable scheduled/manual task execution with task state, run history, leases, retries, and workspace output | | **External tool boundary** | MCP server tools and local Python tools exposed through one runtime surface | Start with the core harness. Turn on heavier production pieces only when the workload needs them. If you prefer guided docs, start with the [Quick Start](https://docs-omnicoreagent.omnirexfloralabs.com/docs/getting-started/quickstart). If you use AI coding tools, use the [AI tools guide](https://docs-omnicoreagent.omnirexfloralabs.com/docs/getting-started/use-docs-with-ai-tools) for Ask AI, `/llms.txt`, hosted docs MCP, Cursor, VS Code, ChatGPT, Claude, and Perplexity. --- ## Quick Start ```bash pip install omnicoreagent ``` ```bash export LLM_API_KEY=your_api_key ``` ```python import asyncio from omnicoreagent import OmniCoreAgent agent = OmniCoreAgent( name="assistant", system_instruction="You are a helpful assistant.", model_config={"provider": "openai", "model": "gpt-4o"}, ) async def main(): result = await agent.run( "Research the top 3 open-source agent runtimes and summarize them.", session_id="quickstart", ) print(result["response"]) await agent.cleanup() asyncio.run(main()) ``` That is the smallest path: one agent, one model, one stable session, the harness loop, session memory, guardrails, workspace files, error handling, and metrics around each run. Context management, tool output offloading, BM25 tool retrieval, subagents, skills, cloud workspace storage, and production backends are opt-in so a small agent stays small. > Ready to go deeper? The [Cookbook](./cookbook) has progressive examples from > hello world to production deployments. --- ## Choose Your Path | Goal | Start Here | |------|------------| | Build your first agent | [Quick Start](#quick-start) | | Add Python tools | [Local tools cookbook](./cookbook/getting_started/agent_with_local_tools.py) | | Connect MCP server tools | [MCP tools cookbook](./cookbook/getting_started/agent_with_mcp_tools.py) | | Manage memory and context | [Getting started cookbook](./cookbook/getting_started) | | Save files, artifacts, and large tool results | [Tool offload cookbook](./cookbook/getting_started/agent_with_tool_offload.py) | | Build a production-shaped app harness | [Real applications cookbook](./cookbook/real_applications) | | Build multi-step workflows | [Workflows cookbook](./cookbook/workflows) | | Serve an agent over HTTP/SSE | [OmniServe cookbook](./cookbook/omniserve) | | Use the docs inside AI tools | [AI tools guide](https://docs-omnicoreagent.omnirexfloralabs.com/docs/getting-started/use-docs-with-ai-tools) | | Debug setup or configuration | [Configuration guide](https://docs-omnicoreagent.omnirexfloralabs.com/docs/how-to-guides/configuration) | | Understand the runtime internals | [Implementation Map](#implementation-map) | --- ## What You Can Build OmniCoreAgent is for application builders who need the agent runtime to hold together after the prototype works. | Build | Harness Pieces You Use | |-------|------------------------| | **MCP-connected product agents** | MCP tools, local tools, structured observations, guardrails, session memory | | **Research and analysis agents** | Parallel tool batches, workspace files, tool offloading, context management, artifact readback | | **Long-running worker agents** | Background tasks, durable task stores, run history, workspace output, retries, cancellation | | **Multi-agent task systems** | Dynamic subagents, shared workspace output, workflow orchestration, telemetry events | | **Agent APIs** | OmniServe REST/SSE, readiness, auth, request timeout, rate limits, metrics | | **Production app integrations** | Optional Redis, MongoDB, SQL, S3, and R2 backends without making the core install heavy | The core idea is simple: one harness entry point, many application membranes. You bring the domain instructions, tools, and business logic. OmniCoreAgent provides the execution boundary around them. --- ## Why It Matters Most demos stop at "LLM plus tool loop." Production agents fail in the layer around that loop: slow sequential tool calls, noisy observations, repeated actions, context exhaustion, unsafe tool output, missing workspace state, uninspectable background work, and weak serving boundaries. OmniCoreAgent exists for that layer. ### 1. Agents call tools in batches instead of forced sequences The usual tool loop looks like this: ```text LLM -> call tool A -> wait -> result -> LLM -> call tool B -> wait -> result ``` OmniCoreAgent lets the model request independent tools together: ```text LLM -> [tool A + tool B + tool C in parallel] -> one structured observation -> LLM ``` The model gets one complete view of the batch before it reasons again. A failed tool is represented beside the successful tools instead of silently collapsing the whole step. Native function calling alone is not the runtime. OmniCoreAgent uses its own tool-call contract, parser, resolver, parallel runner, and result formatter so the harness controls the full execution path. ### 2. Tool results become structured observations Raw tool output is often too noisy for the next reasoning step. Large payloads, errors, irrelevant fields, and prompt-injection content can all distort the loop. OmniCoreAgent routes tool results through an observation pipeline: ```text tool output -> parse -> format -> guardrail check -> offload when configured -> observation -> model ``` The model receives the signal it needs to continue the task, not an unbounded dump of every byte returned by a tool. When tool offloading is enabled, large outputs are written into the active workspace and the model receives a readable preview plus a path it can use later. ### 3. Loop detection uses signatures beyond step counts `max_steps` is still useful, but it is a blunt instrument. It stops an agent that is making progress just as quickly as one that is stuck. OmniCoreAgent tracks SHA256-backed tool-call signatures across the loop. Each signature is based on the tool name, input, and output for the call. The runtime detects: - **Consecutive loops**: the same tool call returns the same result repeatedly. - **Pattern loops**: the same tool repeats a small interaction pattern. When the harness stops a loop, the agent gets a reason. That makes debugging the agent behavior much easier than "max iterations reached." ### 4. The harness is already assembled OmniCoreAgent ships as a working harness, not a bag of disconnected pieces: ```text model + prompt + loop + tools + memory + context + workspace + guardrails + telemetry ``` Keep it small for simple agents, then turn on the heavier harness pieces when the workload needs them: MCP tools, BM25 tool retrieval, dynamic subagents, skills, cloud workspace storage, Redis/Postgres/MongoDB memory, telemetry events, and OmniServe. ### 5. Context is managed before the model call When context management is enabled, OmniCoreAgent checks the active message history before every LLM request. If the configured threshold is crossed, the harness automatically applies the selected strategy before calling the model: ```text messages -> threshold check -> truncate or summarize+truncate -> LLM ``` The system prompt is preserved, recent messages are preserved, and older middle history is either summarized or removed depending on configuration. If you set the budget below your model's real context window, the harness acts before the provider rejects the request. --- ## See It In Action ```python import asyncio from omnicoreagent import MemoryRouter, OmniCoreAgent, ToolRegistry tools = ToolRegistry() @tools.register_tool("search_web") def search_web(query: str) -> dict: """Search the web for information.""" return {"results": [f"Result for: {query}"]} @tools.register_tool("fetch_document") def fetch_document(path: str) -> dict: """Fetch a domain document from an application-owned source.""" return {"path": path, "content": f"Contents of {path}"} agent = OmniCoreAgent( name="research-agent", system_instruction=( "You are a research assistant. Use tools in parallel when the calls are " "independent and you can reason over the results together." ), model_config={"provider": "openai", "model": "gpt-4o"}, local_tools=tools, memory_router=MemoryRouter("in_memory"), agent_config={ "max_steps": 20, "context_management": {"enabled": True}, "tool_offload": {"enabled": True}, "enable_subagents": True, "enable_advanced_tool_use": True, }, ) async def main(): result = await agent.run( "Search for recent AI agent papers and fetch notes.md. Do both at once " "if neither depends on the other." ) print(result["response"]) await agent.cleanup() asyncio.run(main()) ``` The runtime accepts `search_web` and `fetch_document` in the same batch, returns both results together, and continues from one structured observation. --- ## Install Only What You Need ```bash pip install omnicoreagent # Core runtime pip install "omnicoreagent[redis]" # Redis memory backend pip install "omnicoreagent[postgres]" # PostgreSQL / SQL memory pip install "omnicoreagent[mongodb]" # MongoDB memory pip install "omnicoreagent[s3]" # S3 / R2 workspace storage pip install "omnicoreagent[serve]" # OmniServe REST/SSE API pip install "omnicoreagent[tokenizer]" # Token-aware context budgeting pip install "omnicoreagent[otel]" # OTLP trace export pip install "omnicoreagent[langsmith]" # LangSmith trace export pip install "omnicoreagent[opik]" # Comet Opik trace export pip install "omnicoreagent[all]" # Everything ``` Production backends are installable extras. Install only what the agent actually uses. --- ## Features ### Core Runtime | Feature | What It Does | |---------|--------------| | **Parallel Batch Tool Execution** | Executes independent tool calls concurrently and returns one combined observation to the model. | | **Structured Observation Pipeline** | Parses, formats, guardrail-checks, and offloads tool results when configured before the model sees them. | | **Signature-Based Loop Detection** | Detects repeated SHA256-backed tool-call signatures and repeated tool interaction patterns beyond step-count exhaustion. | | **Local Tool Registry** | Registers Python functions as tools with inferred schemas and async/sync execution support. | | **Multi-Tier Memory** | Uses in-memory, Redis, MongoDB, or SQL-backed session history through the memory router. | | **Context Engineering** | Checks context before each model call and automatically truncates or summarizes when the configured budget threshold is crossed. | | **Workspace Files** | Gives agents a local, S3, or R2-backed file workspace for notes, scratchpads, artifacts, and tool offloads. | | **Tool Output Offloading** | Writes large tool results to workspace files and gives the model a preview plus a file reference. | | **Guardrails** | Adds prompt-injection screening inside the observation path with configurable behavior. | ### Production Harness | Feature | What It Does | |---------|--------------| | **Dynamic Subagents** | Lets the main agent spawn focused workers with isolated context and shared workspace output. | | **Durable Background Tasks** | Runs manual or scheduled agent work with task state, run history, retries, cancellation, and workspace output. | | **Workflow Orchestration** | Provides sequential, parallel, and router agents for multi-step application workflows. | | **Telemetry and Traces** | Emits typed telemetry events, retrieves traces by exact `trace_id`, latest session, or `run_id` correlation, and exports traces to OTLP, LangSmith, Opik, or JSONL. | | **OmniServe** | Turns an agent into a REST/SSE service with lifecycle management, auth, rate limits, telemetry APIs, background APIs, and metrics. | ### Integrations | Feature | What It Does | |---------|--------------| | **MCP Native Tools** | Connects MCP servers over stdio, SSE, and Streamable HTTP, including OAuth-capable remote servers. | | **Agent Skills** | Loads packaged capabilities implemented with Python, Bash, or Node.js. | | **BM25 Tool Retrieval** | Selects relevant tools from large tool sets so the prompt stays focused. | | **Runtime Backend Switching** | Switches memory backends at runtime when configured. | | **Universal Models** | Supports OpenAI, Anthropic, Gemini, Groq, Ollama, DeepSeek, Mistral, OpenRouter, Azure, and Cencori through the runtime model layer. | --- ## Implementation Map OmniCoreAgent's capabilities are backed by concrete runtime modules: | Capability | Where It Lives | |-------|----------------| | Parallel tool batches | `core/tools/tool_batch_runner.py` | | XML tool-call contract | `core/agents/xml_parser.py` | | Structured observations | `core/tools/tool_observation.py` | | Tool output offloading | `core/workspace/artifacts.py` | | Automatic context control | `core/agents/llm_step.py`, `core/context_manager.py` | | Workspace files | `core/workspace/tools.py`, `core/workspace/storage.py` | | Dynamic subagents | `core/subagents.py` | | Loop detection | `core/agents/loop_detection.py` | | MCP server tools | `mcp_clients_connection/client.py` | | OmniServe | `serve/` | See the [Agent Harness docs](https://docs-omnicoreagent.omnirexfloralabs.com/docs/core-concepts/agent-harness) for the full implementation map. --- ## Cookbook All examples live in the **[Cookbook](./cookbook)** and are organized by use case. | Category | What You'll Build | |----------|-------------------| | [Getting Started](./cookbook/getting_started) | First agent, tools, memory, telemetry events, and traces | | [Real Applications](./cookbook/real_applications) | Due diligence, support operations, and workspace code review harnesses | | [Workflows](./cookbook/workflows) | Sequential, Parallel, Router agents | | [Background Agents](./cookbook/background_agents) | Scheduled autonomous tasks | | [Production](./cookbook/production) | Guardrails, serving, and production patterns | --- ## Configuration ### Environment Variables For the first run, most hosted model providers only need `LLM_API_KEY`. OmniCoreAgent defaults memory and events to in-memory storage, workspace files to local disk, and optional production integrations stay off until you configure them. ```bash export LLM_API_KEY=your_api_key ``` Add backend-specific variables only when you opt into Redis, MongoDB, SQL database storage, S3, R2, or OmniServe deployment settings. ### Full Harness Config Example The defaults keep the first agent small: workspace files and guardrails are on, conversation memory is in-memory, and advanced harness pieces stay off until you enable them. This example shows the production-style switches together. ```python agent_config = { "max_steps": 15, "tool_call_timeout": 30, "request_limit": 0, # 0 = unlimited "total_tokens_limit": 0, # 0 = unlimited "memory_config": { "mode": "sliding_window", "value": 10000, "summary": {"enabled": False}, }, "enable_workspace_files": True, # Default on "guardrail_mode": "full", # Default "context_management": {"enabled": True}, # Default off "tool_offload": {"enabled": True}, # Default off "enable_advanced_tool_use": True, # Default off "enable_subagents": True, # Default off "enable_agent_skills": True, # Default off } ``` When `enable_subagents` is true, workspace files are enabled automatically so subagents write outputs, notes, todos, and artifacts into the active workspace. > Full reference: [Configuration Guide](https://docs-omnicoreagent.omnirexfloralabs.com/docs/how-to-guides/configuration) --- ## Development ```bash git clone https://github.com/omnirexflora-labs/omnicoreagent.git cd omnicoreagent uv venv && source .venv/bin/activate uv sync --dev pytest tests/ -v pytest tests/ --cov=src --cov-report=term-missing ``` --- ## Troubleshooting | Error | Fix | |-------|-----| | `Invalid API key` | Export `LLM_API_KEY` with the key for the provider selected in `model_config`. | | `ModuleNotFoundError` for Redis / Postgres / MongoDB / S3 | Install the matching extra, for example `pip install "omnicoreagent[redis]"`. | | `Redis connection failed` | Start Redis or use `MemoryRouter("in_memory")`. | | `MCP connection refused` | Ensure the MCP server is running before starting the agent. | > More help: [Basic Usage Guide](https://docs-omnicoreagent.omnirexfloralabs.com/docs/how-to-guides/basic-usage) --- ## Contributing ```bash git clone https://github.com/omnirexflora-labs/omnicoreagent.git cd omnicoreagent uv venv && source .venv/bin/activate uv sync --dev pre-commit install ``` See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. PRs are welcome. --- ## License MIT - see [LICENSE](LICENSE). --- ## Author **Built by [Abiola Adeshina](https://github.com/Abiorh001)**. - **GitHub**: [@Abiorh001](https://github.com/Abiorh001) - **X (Twitter)**: [@abiorhmangana](https://x.com/abiorhmangana) - **Email**: abiolaadedayo1993@gmail.com ### The OmniRexFlora Ecosystem | Project | Description | |---------|-------------| | [OmniMemory](https://github.com/omnirexflora-labs/omnimemory) | Self-evolving memory for autonomous agents | | [OmniCoreAgent](https://github.com/omnirexflora-labs/omnicoreagent) | Production agent harness (this project) | | [OmniDaemon](https://github.com/omnirexflora-labs/OmniDaemon) | Event-driven runtime for running agents as supervised, autonomous infrastructure services | ### Built On [LiteLLM](https://github.com/BerriAI/litellm) - [FastAPI](https://fastapi.tiangolo.com/) - [Redis](https://redis.io/) - [Pydantic](https://docs.pydantic.dev/) --- <p align="center"> <a href="https://github.com/omnirexflora-labs/omnicoreagent">Star on GitHub</a> - <a href="https://github.com/omnirexflora-labs/omnicoreagent/issues">Report Bug</a> - <a href="https://github.com/omnirexflora-labs/omnicoreagent/issues">Request Feature</a> - <a href="https://docs-omnicoreagent.omnirexfloralabs.com/docs">Documentation</a> </p>